Deploying AI agents requires a fundamentally different approach from traditional software rollouts. Organizations move from experimentation to real-world agent deployments. Teams are discovering that success depends less on raw model capability and more on governance, design discipline, and operational rigor. Research cited from Boston Consulting Group underscores growing tensions between autonomy and control, speed and safety, and short-term ROI versus long-term value.
Unlike conventional applications, AI agents operate with varying degrees of autonomy, making governance a foundational requirement rather than an afterthought. Nik Kale, a principal engineer at Cisco, emphasized that agent confidence does not equal accuracy. His teams learned that without early investment in oversight, observability, and human-in-the-loop controls, systems can drift into unintended behaviors as trust grows and scrutiny declines.
Executives interviewed by ZDNET also stressed the importance of starting narrow. Tolga Tarhan, CEO of Atomic Gravity, noted that most successful agents tightly scoped to single domains with clear guardrails and measurable outcomes. High-quality data remains another critical factor. With leaders agreeing that agents perform only as well as the information they are given.
As agent deployments scale, new operational practices are emerging. Martin Bufi of Info-Tech Research Group pointed to the rise of “AgentOps,” a lifecycle-focused methodology covering deployment, monitoring, and continuous improvement. Meanwhile, Sean Falconer, head of AI at Confluent, highlighted context management and adaptability as ongoing challenges, particularly as agents loop through tools and accumulate context over time.
Key takeaways for enterprises:
- Governance and observability must be designed in from day one
- Narrowly scoped agents deliver more reliable outcomes than monolithic designs
- Data quality and context management directly impact agent performance
- “AgentOps” practices are becoming essential for production-scale deployments
As organizations adopt agentic AI, the consensus is clear. Disciplined design, clear boundaries, and adaptable architectures. Not hype-determine whether AI agents become transformative systems or costly experiments.
Source:
https://www.zdnet.com/article/deploying-ai-agents-7-lessons-from-trenches-experts/
